Deterministic household assignment model

ABSTRACT

Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/035,527, filed Jul. 13, 2018, which claims thebenefit of U.S. Provisional Patent App. No. 62/571,823, filed Oct. 13,2017, the disclosures of which are hereby incorporated by referenceherein.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods fordetermining program viewership, and more particularly to systems andmethods for determining the demographics of viewers of programs usingdeterministic household assignment.

BACKGROUND

Advertising relies on program and network viewership data in order todetermine the reach and impressions of targeted advertisement.Advertisers are interested in numbers of viewers as well as thedemographics of viewers in order to effectively manage advertisingtiming and content. Understanding audience viewing and habits may beuseful in supporting planning, buying, and selling advertising.

Therefore, there is a need for improved systems and methods fordetermining the demographics of viewers of content using deterministichousehold assignment.

SUMMARY

Techniques for projecting household-level viewing events are describedherein. Initially, population data may be accessed including classes ofa plurality of demographic attributes for households in a market. Anarray of representative household units (RHUs) may be generated, and theRHUs may be assigned a class for each of the demographic attributes anda quota based on the demographic attributes of the population data. Apanelist class may be accessed for each of the demographic attributes ofa plurality of panelist households. Each of the panelist households maybe assigned to one of the RHUs based on at least one of the panelistclasses matching the classes for respective demographic attributes ofthe RHU, and the number of matching panelist households assigned to eachof the RHU may be based on the quota. Panelist viewing data representingviewing events associated with the panelist household may be accessed. Areport may be generated with the classes of the RHUs and the panelistviewing data of the assigned panelist households.

In some embodiments, assigning the panelist households to one of theRHUs may be based on each of the panelist classes matching the classesfor the respective demographic attributes of the RHU. In someembodiments, the panelist viewing data may include an identification ofa displayed media, advertisement, website, app, network and/or programand a time duration of the viewing event. In some embodiments, theviewing event may be displayed on one or more of a television, a mobilephone, a tablet, a laptop computer, a desktop computer, smartappliances, and a smart watch. In some embodiments, the demographicattributes may include one or more of a television stratum, a presenceof a DVR, and a number of television sets.

In some embodiments, the demographic attributes may include one or moreof an age of at least one member of the household, a race of at leastone member of the household, an ethnicity of at least one member of thehousehold, and an education level of at least one member of thehousehold. In some embodiments, the demographic attributes may includeone or more of an income of the household, a language spoken in thehousehold, a number of members of the household, and a number ofchildren of the household.

In some embodiments, the instructions, when executed, may further causethe at least one processor to determine that the panelist households areactive based on viewing data accessed from a predetermined period oftime, wherein only active panelist households are assigned to the RHUs.In some embodiments, the instructions, when executed, may further causethe at least one processor to generate the quota based on the number ofhouseholds with the demographic attributes of the RHU relative to thenumber of households in the market. In some embodiments, theinstructions, when executed, may further cause the at least oneprocessor to stop assigning panelists households to an RHU based on thenumber of matching panelist households meeting the quota of the RHU.

In some embodiments, the instructions, when executed, may further causethe at least one processor to duplicate viewing data of the panelistshouseholds for an RHU based on the number of matching panelisthouseholds assigned to the RHU being less than the quota after theplurality of panelist households are assigned. In some embodiments, thepopulation data may be received from one or more of a credit bureau anda census bureau. In some embodiments, the instructions, when executed,may further cause the at least one processor to receive a known value ofviewing data for the market, and adapt the panelist viewing data for atleast one of the RHUs based on the known value of the viewing data. Insome embodiments, the instructions, when executed, may further cause theat least one processor to receive second population data for at leastone second market, and scale the panelist viewing data for at least oneof the RHUs of the market based on a relative size of the populationdata compared to the second population data.

The assignment of the panelist households may be based on first andsecond demographic attributes. The RHUs may be assigned a class for eachof the first and second demographic attributes. Panelist classes of thefirst and second demographic attributes for a first panelist householdmay be matched to the respective classes of the first and seconddemographic attributes for a first RHU. The first panelist class maythen be assigned to the first RHU. The panelist classes of the first andsecond demographic attributes for a second panelist household may bedetermined to not match the respective classes of the first and seconddemographic attributes for any RHU.

The panelist class of the first demographic attribute for the secondpanelist household may be matched to the class of the first demographicattribute for the first RHU. The second panelist household may then beassigned to the first RHU. The report may be generated including theclasses of the first RHU and the panelist viewing data of the first andsecond panelist households.

In some embodiments, the first demographic attribute may include one ormore of an income of the household, a language spoken in the household,a number of members of the household, and a number of children of thehousehold. In some embodiments, the second demographic attribute mayinclude one or more of an age of at least one member of the household, agender of at least one member of the household, a race of at least onemember of the household, and an education level of at least one memberof the household.

In some embodiments, the population data may include classes for a thirddemographic attributes of the households in the market, the RHUs may beeach assigned a class for the third demographic attribute. A panelistclass for each of the first, second, and third demographic attributesmay be accessed for a third panelist household. Accordingly, theinstructions, when executed, may further cause the at least oneprocessor to: determine that the panelist classes of the first, second,and third demographic attributes for the third panelist household do notmatch the respective classes of the first, second, and third demographicattributes for any RHU; determine that the panelist classes of the firstand second demographic attributes for the third panelist household donot match the respective classes of the first and second demographicattributes for any RHU; match the panelist class of the firstdemographic attribute for the third panelist household to the class ofthe first RHU for the first demographic attribute; and assign the thirdpanelist household to the first RHU. In some embodiments, the thirddemographic attribute may include a number of television sets.

Embodiments of any of the described techniques may include a method orprocess, an apparatus, a device, a machine, a system, or instructionsstored on a computer-readable storage device. The details of particularembodiments are set forth in the accompanying drawings and descriptionbelow. Other features will be apparent from the following description,including the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system in which viewership informationmay be collected and processed to determine and/or estimate panelistviewing data and assign panelist households.

FIG. 2 illustrates an exemplary system in which household-level viewingdata can be used to project market-level data through householdassignment.

FIG. 3 is a flow chart illustrating an exemplary process for generatinga report with market-level data projected from household-level viewingdata.

FIG. 4 illustrates a schematic illustrating an exemplary array ofdemographic attributes for Residential Household Units (RHUs).

FIG. 5 illustrates a schematic illustrating quotas of a functionalcross-section of RHUs.

FIG. 6 is a flow chart illustrating an exemplary process for assigningpanelist households to RHUs.

DETAILED DESCRIPTION

The methodology involves creating an array of Representative HouseholdUnits (RHUs), demographically and behaviorally balanced to represent ageographic market. These RHUs may become the recipient dataset intowhich disparate donor datasets are assigned. The RHUs may beproportional to the overall geographic market (e.g., a ratio of 1 per 10households in the market) and assigned demographic and/or behaviorcharacteristics of the market at large. The behavior and/or demographiccharacteristics of the market can be established from population datareceived over a network from a trusted third party, such as data fromone or more census bureaus and/or credit bureaus. The population datamay provide invaluable granular data on the general make-up of a marketat scale, but viewership data at the market-scale is often incompleteand/or inaccurate.

Therefore, real data from different datasets at a smaller scale (e.g.,household-scale, person-scale, and/or device-scale) may be incorporated.The real data may be obtained from content viewed on devices, such astelevisions (TVs), tablets, mobile phones, and/or other electronicdevices. The viewing data may be accessed from “panelist households”who, in at least some cases, have agreed to have their viewing behavioractively and/or passively, directly monitored. For example, televisionviewership of the panelist household may be measured by a set-top-box(STB) logging viewing activity. Due to the direct access to the STB, theviewing data may provide a rich dataset accurately detailing viewingevents of the members of the panelist households. The viewing dataand/or panelist households may also be maintained current by providing athreshold of activity. For example, only panelist households withviewing data within the past 30 or 60 days may be assigned to the RHUs.The viewing data may also incorporate other devices (e.g., an iPad)associated with the panelist household by connecting to a householdnetwork router. The panelist viewing data may further include viewingdata of devices (e.g. mobile phones) that are registered to a member ofthe panelist household and accessed data through a cellular network. Thepanelist viewing data may include return path data (RPD), which is apassive data collection technique that collects any user/viewer activitycollected from a device defined by a start time and a duration. Thepanelist viewing data provides a rich-set of data of the media that realindividuals are consuming. However, since the panelist viewing data ismainly received from a self-selecting population (panelists), thepanelist viewing data itself does not provide an indication of viewingdata of an overall geographic market.

Household data may also be maintained and include demographic attributesfor each of the panelist households and associated members, such ashousehold income, number of members of the household, the gender of themembers, the age of the members, and strata of television access. Thedemographic attributes from the panelist households may be mapped to theRHUs of the population data to populate the RHUs with real panelisthouseholds. The viewing data of the panelist households may be assignedto the RHUs based on matching demographic attributes, such that actualviewing data from a real household may be assigned to a demographically,behaviorally matched RHU. Therefore, the viewing data of the panelisthouseholds may be proportionally calibrated to accurately represent themarket population.

To maintain the demographic make-up of the market, each of the RHUs maybe assigned a quota based on the overall population of the market. Forexample, if, according to the population data, households of two members(Male aged 25-34 years old; Female aged 25-34 years old) have a higherpopulation than households of one member (Male aged 25-34 years old),RHUs of the first households may be assigned a proportionally higherquota than the RHUs of the second households. Assignment of the panelisthouseholds to the RHUs may be repeated until the respective quota isreached and stopped after the quota is reached, while the remaining RHUsmay be populated until the respective quota is reached.

Perfect matches between the panelist households to the RHUs may beprioritized. However, some degree of inference and ascription may berequired during the assignment phase in cases where either (1) there arenot enough exact matches for the RHUs and/or (2) the raw unit assignedis not behaviorally complete (e.g., the donor household doesn't have asmany set top box devices as have been designated for the RHU; or if DVRrecords are absent). In these cases, the match may be constrained to asmany demographics and attributes as available, and the best remainingraw record may be assigned based on closest distance behaviorally.Assignments made to a particular RHU may be maintained if the raw inputis still available (e.g., not attrited in the data set). This maintainslongitudinal consistency in behavioral profiles. The goal of theassignment process is to achieve most of the target unique visitationand additive totals directly without additional allocations oradjustments (the target is 85% of market-network-day hours for TV). Whenthe data is insufficient for the RHUs, matching of panelist householdsmay be relaxed by removing certain demographic attributes from therequirements of being assigned to an RHU. The core demographicattributes may be prioritized to maintain the integrity of the coredemographic definition of the RHU. For example, one or more deviceattributes (e.g., number of devices) may be relaxed first, one or moremember attributes (e.g., age of the members, race of the members, and/oreducation level of the members) may be relaxed second, and one or morehousehold attributes (e.g., income of the household, language spoken inthe household, number of members, and/or number of children) may berelaxed third. In some embodiments, if RHUs do not have sufficientmatches of panelist households to reach the quota, viewing data ofpreviously assigned panelist households may be duplicated to ensure thatthe panelist viewing data of the RHUs is correctly proportional.

After assignment of the panelist households to the RHUs, viewing datareceived directly from the panelist households may be readily assignedto the respective RHU. The result is a massive respondent-level datasetthat projects back to universe, matches individual currency measuresfrom component data sets. Reports may be generated from the data anddisplayed to provide an accurate measure of demographic-based viewingdata for the overall market to the content providers, advertisers, andothers. The reports may therefore be used to estimate the number ofviewing people and/or households of a particular demographic for aparticular program, advertisement, sporting event, and/or other contentitem.

The resultant dataset and reports may further be adapted based onavailable known and trusted reported results from third parties. Forexample, the system may compare the projected assigned viewing data andadditives to the individual platform targets (e.g., available as eithercensus totals or a combination of census and enumeration) and may assignindividual events into the appropriate RHUs to hit the targets. Thisassures that the projected results in the system matchindividual-currency reported results. The added events may be actualevents from the pool of previously unassigned activity. The specificrules and targets for triggering the adaptive process may be determinedby the individual platforms but generally an incremental event may beadded to an RHU that shows a high propensity for the type of event andhas a gap in activity that can accept the event. The result of theadaptive process is an individual respondent level profile that isempirically valid and the aggregation of those profiles achieves thecore platform targets (e.g., market-network-day hours for TV). Thisadaptive process may be run daily and does not guarantee longitudinalconsistency of assignment of events across RHUs or raw households (thatis, a representative RHU is not guaranteed to get an adaptive eventevery day, nor will it get an event from the same sourcehousehold/person/device every day).

FIG. 1 illustrates an example of a system 100 in which viewershipinformation may be collected and processed to determine and/or estimateaudience measurement data. The system 100 may include a number ofpanelist households 101, such as the illustrated panelist householdassigned the Identification Number 1231. The panelist households 101 mayinclude one or more panelist devices 112 for viewing content by one ormore members 102. The panelist devices 112 may be embodied by and/or beconnected with any number of a television, a mobile phone, a tablet, alaptop computer, a desktop computer, smart appliances, and/or a smartwatch. For example, the panelist device 112 may include a number ofdifferent types of devices associated with the panelists household 101,such as a television in the household 101, a digital video recorder(DVR) connected to the television, a set-top-box (STB) associatedconnected to the television, and/or a home network router.

The panelist devices 112 may record panelist viewing data 116 forviewing events displayed on the panelist devices 112 or an associateddisplay. The viewing event may indicate a media, an advertisement, awebsite, an app, a network and/or a program transmitted to the panelistdevice 112, and/or a time duration that the panelist household 101 wasexposed to the media, an advertisement, a website, an app, a networkand/or a program. The panelist devices 112 may report the panelistviewing data 116 to a usage collection server 114, and the panelistviewing data 116 may be stored in a storage device 120. In addition toviewing events, the panelist viewing data 116 may include datacorresponding to the panelist household 101, the panelist device 112,stream control data, data representing content recorded by the panelistdevice 112, programs ordered on the panelist device 112 through an ondemand service, and/or data about when the panelist device 112 wasturned on or off. Other data about the status of the panelist device 112and user interaction with the panelist device 112 may also be recordedand included in the panelist viewing data 116.

In some embodiments, the panelist devices 112 may include an STB thattransmits television programs to a display (e.g., a television) fromvarious stratum, such as over the air (OTA), direct broadcast satellite(DBS), cable, and/or telephone companies (telco). Thus, the panelistviewing data 116 may include tuning data recorded by the STB indicatingmedia, advertisements, website, app, network and/or program beingtransmitted to the television and a time duration. The panelist devices112 may also include a household network router of the panelisthousehold 101 that monitors access of a network (e.g., the Internet) bycomputers, smart phones, and/or tablets in the household 101. Thehousehold router may monitor viewing events of the household 101 andreport panelist viewing data 116 to the usage collection server 114. Thepanelist devices 112 may further include portable devices (e.g., mobilephones) physically located outside of the panelist household 101. Such apanelist device 112 may be associated with the panelist household 101 bybeing registered to one of the members 102, and the panelist device 112may monitor viewing events on the panelist device 112 and reportpanelist viewing data 116 to the usage collection server 114.

In some embodiments, the panelist devices 112 may, additionally oralternatively, generate panelist viewing data 116 by monitoring mediaviewed by the member 102 while carrying the panelist device 112. Forexample, the panelist device 112 may include a microphone to capture andanalyze ambient audio information to determine a likelihood that themember 102 is watching a particular television program. In some cases,the panelist device 112 may extract encoded signals from the soundinformation identifying the particular television program being watchedby the member 102. The panelist device 112 may also identify theparticular television program from the sound information using othermechanisms, such as, for example, by generating acoustic fingerprintfrom the sound information in querying a storage device mapping knownacoustic fingerprints to television programs. In some embodiments, thepanelist device 112 may monitor other types of information to determinea television program being watched by the member 102, such as, forexample, video information, radio frequency (RF) signals, infrared (IR)signals, or other information. The panelist viewing data 116 generatedfrom the panelist devices 112 in this manner may be saved associatedwith the panelist household 101 and/or member 102 associated with thepanelist device 112.

The panelist devices 112 may produce panelist viewing data 116representing viewing activity by the members 102. In some embodiments,the panelist device 112 may provide panelist viewing data 116 directlyto the storage device 120. The panelist devices 112 may, additionally oralternatively, provide the panelist viewing data 116 to a separatecollection server or set of servers, and the panelist viewing data 116may be acquired by or otherwise stored in the storage device 120. Insome embodiments, the panelist viewing data 116 may include informationregarding television viewing events, such as, for example, a televisionprogram being watched, a television network, an entity operating thetelevision network, a start time and stop time for the televisionviewing event, an identifier of the member 102 associated with thetelevision viewing event, and/or other information.

The members 102 may be associated with demographics, such as age,gender, race, ethnicity, income, education level, and these demographicsmay be collected and stored in the storage device 120 or another storageas panelist household data 110. In the example illustrated in FIG. 1 ,the panelist household 101 includes four members 102: an 18-year-oldmale, a 24-year-old female, a 35-year-old female, and a 46-year-oldmale. The specific age and/or gender of the members 102 may be stored inpanelist household data 110. The demographic attributes of the members102 may, additionally or alternatively, be associated with demographicpanelist classes. For example, each member 102 may be associated withone of a panelist class for age (e.g., 18-24, 25-34, 35-44, 45-54,55-64, or 65+), rather than a specific age. This information may also bestored in the panelist household data 110. Other demographic attributesof the members 102 may be collected, such as occupation, income, raceand/or ethnicity. Similarly, these demographic attributes may be savedto the panelist household data 110 based on a plurality of panelistclasses (e.g., income of $0-$25,000, $25,001-$50,000, $51,000-$75,000,$75,001-$100,000 . . . $300,000+).

The demographic attributes of individual members 102 of the household101 may be aggregated into household demographic attributes andassociated panelist classes, which are stored in the panelist householddata 110. For example, the income of the members 102 may be aggregatedto determine a household income. Additional household demographicattributes may be a language spoken in the household 101, a number ofmembers 102 of the household 101, and a number of children of thehousehold 101, and panelist classes may be generated for each of thehousehold demographic attributes. In addition, a geographic area orlocation for the panelist household 101 may be stored in the panelisthousehold data 110. The geographic area or location for the panelisthousehold 101 may be saved according to a geographic market (e.g., oneof 210 Designated Market Areas (DMAs) assigned by Nielsen). The panelisthousehold data 110 may further include device demographic attributes,for example, one or more of a television stratum, a presence of adigital video recorder (DVR), a number of television sets of thehouseholds 101, and types of the panelist devices 112 associated withthe household 101.

The demographic information for the household members 102 and/orpanelist households 101 may be collected in a number of ways. Forexample, the panelist households 101 may be recruited to be part of atelevision viewing panel that is used to provide panelist viewing data116. Once the panelist household 101 is recruited, the demographicinformation may be collected as part of a registration process. Inanother example, the panelist household 101 may be a part of, orrecruited into, an Internet usage panel that is used to provide Internetusage data. Demographic information of the household members 102 may becollected when the panelist household 101 is registered to be part ofthe Internet usage panel. As part of the Internet usage panel, thepanelist household 101 may have a panel application installed on one ormore of the panelist devices 112 in the panelist household 101. Thepanel application may collect television and/or internet usage data tosend to the usage collection server 114. In some embodiments, theinternet usage data could be used to infer information about householdmember 102, such as by comparing internet content accessed by eachmember 102 with demographic or other information about users accessingthe same content. Other methods may be used to capture or confirminformation about members 102 of the panelist household 101, such assurvey data or data captured from other household behaviors, or dataprovided by third party services that attempt to determine demographicdata of household members 102.

The storage device 120 may further receive population data 118 over anetwork 122. The population data 118 may be received from one or moretrusted third party sources, such as one or more census bureaus and/orcredit bureaus. The population data 118 may include demographic data(e.g., age, gender, ethnicity, race, and/or income) of constituents ofhouseholds of a market. The population data 118 may also includeresidential information for the constituents, such as information on amale, aged 35 with an income of $35 k, and living at 335 Main Street,Charleston, S.C. 24901. The population data 118 may be based ongeographic markets (e.g., according to Nielsen) and aggregated based onhousehold and/or demographic attributes. For example, the populationdata 118 may include aggregated data, such as there being 500 householdsin the Charleston market with a male member aged 35-44 having an incomeof $25,001-$50,000. In some instances, the population data 118 may alsoprovide limited viewing data associate with demographics, households,and/or constituents.

FIG. 2 illustrates an example of a system in which household-levelviewing data may be used to generate projected market-level viewing datathrough demographic attribution. The system 200 includes a reportingserver 202 embodied, for example, by a general-purpose computer capableof responding to and executing instructions in a defined manner, apersonal computer, a special-purpose computer, a workstation, and/or amobile device. The reporting server 202 may receive instructions from,for example, a software application, a program, a piece of code, adevice, a computer, and/or a computer system, which independently orcollectively direct operations. The instructions may be embodiedpermanently or temporarily in any type of machine, component, equipment,or other physical storage medium that is capable of being used by thereporting server 202.

The reporting server 202 may have a processor that executes instructionsimplemented by a pre-processing module 204, an RHU generation module206, a household assignment module 208, and a report generation module210. The reporting server 202 may be operable to process the panelisthousehold data 110, panelist viewing data 116, and population data 118to generate one or more reports 212 that include panelist viewing data116.

FIG. 3 is a flow chart illustrating an exemplary process 300 forgenerating the reports 212. The following describes the process 300 asbeing performed by components of the reporting server 202 with respectto data associated with the panelist household 101. However, the process300 may be performed by other systems or system configurations andimplemented with respect to other members of the viewing audience.

At step 302, the pre-processing module 204 may access a portion of thecollected data, including the population data 118. The pre-processingmodule 204 may perform one or more pre-processing functions on thepopulation data 118 as appropriate. In some cases, the pre-processingmodule 204 may identify particular demographic attributes of thepopulation data 118, such as age, gender, race, occupation, geographicarea, and/or other elements associated with the population. In somecases, the pre-processing module 204 may sort the population data intoparticular demographic attributes based on the particular member 102associated with each viewing event in the panelist viewing data 116. Insome cases, the pre-processing module 204 may examine the distributionof the population data, and generate classes based on the one or more ofhousehold demographic attributes, member demographic attributes, and/ordevice demographic attributes. The household demographic attributes mayinclude one or more of an income of the household, a language spoken inthe household, a number of members of the household, and a number ofchildren in the household. The member demographic attributes may includeone or more of an age of at least one member of the household, a race ofat least one member of the household, and an education level of at leastone more of the household. The device demographic attributes may includeone or more of a television stratum, a presence of a digital videorecorder (DVR), and a number of panelist devices 112. The populationdata 118 may be received from the network 122 based on the geographicmarket (e.g., assigned by Nielsen), or alternatively, the pre-processingmodule 204 may categorize the households into markets based on anassociated location, street, and/or address.

At step 304, the RHU generation module 206 may generate an array of RHUsfor each of the markets based on the population data. The RHUs may begenerated based on any number of demographic attributes 402. Asillustrated in FIG. 4 , the demographic attributes 402 of the RHUs mayinclude a number of members 102 in the household 101, a gender of themembers 102, an income of the household 101, a number of television sets112 of the household 101, and/or a television stratum. Each RHU may thenbe assigned classes 404 for each of the demographic attributes 402. Theclasses 404 may include a single value or a range of values for each ofthe demographic attributes 402. For example, the classes 404 withlimited number of probable values (e.g., number of television setsand/or gender) may be based on a single value, but the classes 404 witha larger number of probably values (e.g., age and/or income) may bebased on a range of values.

The RHU generation module 206 may then generate a quota for each of theRHUs of the market. The quota may be a representative number ofhouseholds in each RHU proportionally based on the distribution ofdemographic attributes in the population data 118. For example, FIG. 5illustrates a functional cross-section of the RHUs having 2 members, amale between 25-34 years old, a female between 25-34 years old, ahousehold income of $25,001-50,000, and 2 television sets. As furtherillustrated by the outlined images of houses, the number of panelisthouseholds to be assigned to the RHU with DBS and DVR is 12, the numberassigned to the RHU with Cable/Telco and DVR is 18, and the numberassigned to the RHU with OTA and DVR is 6. The number of panelisthouseholds to be assigned to the RHUs of DBS without DVR is 6, thenumber assigned to the RHU with Cable/Telco without DVR is 9, and thenumber assigned to the RHU with OTA without DVR is 3. The illustratedquotas for each of the RHUs is based on the relative proportion of thepopulation falling within these classes. For example, as illustrated,the population data 118 may indicate that there are about twice as manyhouseholds of (2 Members, M25-34 F25-34; $25,001-50,000; 2 televisionsets) with DBS that have a DVR than do not have a DVR. The illustratedquotas are also based on the population data 118 indicating that thereare about two-thirds as many households of (2 Members, M25-34 F25-34;$25,001-50,000; 2 television sets) with DBS and a DVR than Cable/Telcoand a DVR. The quotas would therefore proportionally reflect thedemographics of the market as indicated in the population data 118.

At step 306, the household assignment module 208 may access thehousehold data 110 for panelist classes for demographic attributes of aplurality of panelist households 101. As discussed herein, the classesmay be collected directly from the panelist households 101 and stored inthe storage device 120. The panelist household data 110 may includestored data of the panelist households 101, including classes forhousehold demographic attributes, member demographic attributes, and/ordevice demographic attributes. The panelist household data 110 may alsoinclude an activity log for the panelist households based on thepanelist viewing data 116. For example, the panelist household data 110may indicate whether the panelist households 101 have been inactivewithin the 7, 30, or 60 days.

At step 308, the household assignment module 208 may determine that thepanelist households 101 are active within a predetermined period oftime. For example, the household assignment module 208 may modify a listof the panelist households of step 306 by deleting the panelisthouseholds without active viewing data within the past 30 days. Removinginactive panelist households 101 may avoid distortion of the panelistviewing data due to non-reporting and/or inactive panelist households101.

At step 310, the household assignment module 208 may assign eachpanelist household 101 to one of the RHUs based on at least one of thepanelist classes matching the classes for respective demographicattributes of the RHU. For example, as illustrated in FIG. 5 , thehousehold assignment module 208 may assign panelist households 101(shown as filled in houses) based on the quota (shown as outlinedhouses). The household assignment module 208 may assign panelisthouseholds 101 to RHUs until the quota of the RHU is reached. As furtherillustrated in the exemplary flow chart of FIG. 6 , the householdassignment module 208 may assign first, second, and third panelisthouseholds 101 to one or more RHUs.

At step 320, the household assignment module 208 may match panelistclasses for first, second, and third demographic attributes of the firstpanelist household 101 to respective classes of a first RHU. At step322, the household assignment module 208 may assign the first panelisthousehold to the first RHU based on the matching of the first, second,and third demographic attributes. For example, the panelists classes(e.g., 2 members, a male between 25-34 years old, and 2 television sets)of the first, second, and third demographic attributes for the firstpanelist household may match the respective classes (e.g., 2 members, amale between 25-34 years old, and 2 television sets) of the first RHU.Thus, the first panelist household 101 may be assigned to the first RHU.

At step 324, the household assignment module 208 may determine thepanelist classes of the second panelist household do no match respectiveclasses of any RHU for the first, second, and third demographicattributes. However, at step 326, the household assignment module 208may match the panelist classes for the first and second demographicattributes of the second panelist households to respective classes ofthe first RHU. At step 328, the household assignment module 208 mayassign the second panelist household to the first RHU. For example, thepanelists classes (e.g., 2 members, a male between 25-34 years old, and10 television sets) of the first, second, and third demographicattributes for the second panelist household does not match therespective classes for any RHU. However, the panelists classes (e.g., 2members and a male between 25-34 years old) of the first and seconddemographic attributes for the second panelist household does match therespective classes for the first RHU. Thus the second panelist householdis assigned to the first RHU.

At step 330, the household assignment module 208 may determine thatpanelist classes for the first and second demographic attributes of thethird panelist household do not match respective classes of any RHU. Thehousehold assignment module 208 may match the panelist class for thefirst demographic of the third panelist household to the respectiveclass of the first RHU. At step 334, the household assignment module 208may assign the third panelist household to the first RHU. For example,the panelists classes (e.g., 2 members, a male between 96-100 years old)of the first and second demographic attributes for the third panelisthousehold does not match the respective classes for any RHU. However,the panelists classes (e.g., 2 members) of the first demographicattribute for the third panelist household does match the respectiveclasses for the first RHU. Thus, the second panelist household isassigned to the first RHU.

For example, the first demographic attribute may be a householdattribute, such as one or more of an income of the household, a languagespoken in the household, a number of members of the household, and anumber of children in the household. The second demographic attributemay be a member attribute, such as one or more of an age of at least onemember of the household, a gender of at least one member of thehousehold, a race of at least one member of the household, an ethnicityof at least one of the household, and an education level of at least onemember of the household. The third demographic attribute may be a deviceattribute, such as one or more of a number of panelist devices of thehousehold, a television strata, and a presence of a digital videorecorder (DVR).

Perfect matches of panelist households may be prioritized to provide amore accurate representation of the RHU. After determining the number ofperfect matches (e.g., matching classes for the first, second, and thirddemographic attributes) is not sufficient to meet the quota of the RHU,the household assignment module 208 may selectively “relax” or disregarddemographic attributes to assign the panelist households 101. Thus, oneor more device demographic attributes (e.g., number of television setsin the panelist household 101) may be removed from consideration, asillustrated in steps 324-326. Then, if necessary, one or more memberdemographic attributes (e.g., the ethnicity of one of the members 102)may be disregarded, as illustrated in steps 330-332. Then one or morehousehold demographic attributes (e.g., income of household 101) maypotentially be removed from consideration.

Although FIG. 6 illustrates first, second, and third demographicattributes, the process 300 may include any number of demographicattributes. The process may include just the first and seconddemographic attributes. The process 300 may, additionally oralternatively, include a plurality of one or more of the first, second,and third demographic attributes. For example, the process 300 mayinclude two first demographic attributes, two second demographicattributes, and two third demographic attributes, and proceed similar tosteps 320-334, iteratively removing one of the third, second, and thirddemographic from consideration in order to assign the panelisthouseholds 101 to the RHUs. Although FIG. 6 illustrates the first,second, and third panelist households 101 being assigned to the firstRHU, the panelist households 101 may be assigned in any arrangement. Forexample, the first panelist household 101 may be assigned to a third RHUin step 334. The second panelist household 101 may be assigned to asecond RHU in step 328, and the third panelist household 101 may beassigned to the first RHU in the step 322.

The assignments of steps 320-334 may proceed until a quota for the RHUsare met. For example, the quota for RHU 1 may be met in step 322, whenthere are sufficient number of panelist households 101 with matchingpanelist classes for the first, second, and third demographicattributes. The assignment for RHU 1 would then stop due to the quotabeing met. However, the assignment for RHU 2 may proceed through steps324-328, for example, when RHU 2 is not as well represented in thepanelist households 101 as RHU 1. Steps 320-334 may be performed foreach of the RHUs of the market in order to provide panelist householdassignments that proportionally matches the demographic attributes ofthe market.

In some embodiments, the household assignment module 208 may duplicatematching panelist households 101 of an RHU based on the number ofmatching panelist households 101 assigned to the respective RHU beingless than the quota. In this instance, the panelist households 101 withthe best match to the respective RHU (e.g., the most matching classes)may be duplicated to provide an improved representation of the RHU. Theduplication of the panelist households may ensure that the quota is metfor each of the RHUs, while maintaining the demographic integrity of theRHUs.

At step 312, the report generation module 210 may access panelistviewing data 116 representing viewing events associated with thepanelist households 101. At step 314, the report generation module 210may generate viewership reports 212 with the RHUs and the panelistviewing data 116 of the assigned panelist households 101. The reports212 may include data at any level of aggregation, and may be specifiedby a demographic attributes of the RHUs. The reports 212 may include thepanelist viewing data 116 of various demographic groups as estimatedthrough the use of demographic attribution. For example, ahousehold-based report 212 may indicate that 10% of households thatprimarily speak Spanish watch soccer between 7 and 8 pm on Wednesday or25% of households with at least one child watch Peppa Pig. Amember-based report 212 may indicate that 8% households with at leastone member having a Ph.D. watched PBS. A device-based report 212 mayindicate that 20% of households without a DVR watch NBC duringprime-time. The reports 212 based on the RHUs may include as manydemographic attributes as desired. The reports 212 may provide accurateviewing data obtained directly from panel devices 112 in panelisthouseholds 101, accurately scaled based on the demographics of themarket population.

The reports 212 may be displayed on a graphical user interface (GUI) onany type of device. The reports 212 may be generated from the data anddisplayed to provide an accurate measure of demographic-based viewingdata for the overall market to the content providers, advertisers, andothers. The reports 212 may therefore be used to estimate the number ofviewing people and/or households of a particular demographic for aparticular program, advertisement, sporting event, and/or other contentitem.

In some embodiments, the reports 212 of a market may be scaled relativeto one or more other markets. For example, a first report 212 may begenerated based on the New York City market and a second report 212 maybe based on the Washington, D.C. market. The first and second reports212 may be combined by scaling the reports 212 based on the relativeoverall population of the market and integrating. Thus, the first report212 may be multiplied by a factor of the population of the New York Citymarket relative to the Washington, D.C. market, and added to the secondreport 212 to combine the two markets.

At step 316, the report generation module 210 may adapt the reports 212based on empirical viewing data. The reports 212 may be compared to theprojected assigned viewing data to the individual platform targets, suchas known viewing data for the market made available as either censustotals or a combination of census and enumeration. The targets mayprovide a market data set with a high-confidence level for accuracy. Asa result of the comparison, the report generation module may assignindividual events into the appropriate RHUs to reach the targets of theknown empirical data. The assigned individual events may be actualviewing events from the panelist viewing data 116, which were previouslyunassigned to an RHU. The adaption of step 316 may assure that theprojected results of the reports 212 match individual-currency reportedresults of known empirical data.

Although specific examples using various equations of probability aredescribed herein, the methods described herein can be used with avariety of probability and statistical techniques and are not limited toonly the equations and examples shown.

The techniques described herein can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The techniques can be implemented as a computerprogram product, such as a computer program tangibly embodied in aninformation carrier, e.g., in a machine-readable storage device, inmachine-readable storage medium, in a computer-readable storage deviceor, in computer-readable storage medium for execution by, or to controlthe operation of, data processing apparatus, e.g., a programmableprocessor, a computer, or multiple computers. A computer program can bewritten in any form of programming language, including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer program canbe deployed to be executed on one computer or on multiple computers atone site or distributed across multiple sites and interconnected by acommunication network.

Process steps of the techniques can be performed by one or moreprogrammable processors executing a computer program to performfunctions of the techniques by operating on input data and generatingoutput. Process steps can also be performed by, and apparatus of thetechniques can be implemented as, special purpose logic circuitry, e.g.,an FPGA (field programmable gate array) or an ASIC (application-specificintegrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, such as,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as, EPROM, EEPROM, and flash memorydevices; magnetic disks, such as, internal hard disks or removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated inspecial purpose logic circuitry.

A number of embodiments of the techniques have been described.Nevertheless, it will be understood that various modifications may bemade. For example, useful results still could be achieved if steps ofthe disclosed techniques were performed in a different order and/or ifcomponents in the disclosed systems were combined in a different mannerand/or replaced or supplemented by other components.

What is claimed is:
 1. A system, comprising: a panelist device executingpanelist software configured to monitor viewing events displayed on thepanelist device for a panelist household and to generate panelistviewing data based on the monitored viewing events; another devicecomprising: at least one processor; and at least one memory storinginstructions that, when executed, cause the at least one processor to:access population data including classes for each of first, second, andthird demographic attributes of households in a market; generate anarray of representative household units (RHUs) including a first RHU,wherein the RHUs are each assigned a class for each of the first,second, and third demographic attributes; access, based on the panelistviewing data, a panelist class of each of the first, second, and thirddemographic attributes for first, second, and third panelist households;match the panelist classes of the first, second, and third demographicattributes for the first panelist household to the respective classes ofthe first, second, and third demographic attributes for the first RHU;assign the first panelist household to the first RHU; determine that thepanelist classes of the first, second, and third demographic attributesfor the second panelist household do not match the respective classes ofthe first, second, and third demographic attributes for any RHU of thearray of RHUs; match the panelist class of the first and seconddemographic attributes for the second panelist household to the class ofthe first and second demographic attributes for the first RHU; assignthe second panelist household to the first RHU without considering thethird demographic attribute; determine that the panelist classes of thefirst and second demographic attributes for the third panelist householddo not match the respective classes of the first and second demographicattributes for any RHU of the array of RHUs; match the panelist class ofthe first demographic attribute for the third panelist household to theclass of the first RHU for the first demographic attribute; assign thethird panelist household to the first RHU without considering the secondand third demographic attributes; and determine, based at least on thepanelist viewing data and the assignment of the first panelisthousehold, the second panelist household, and the third panelisthousehold to the first RHU, a number of unique views of content withoutadditional adjustment.
 2. The system of claim 1, wherein the firstdemographic attribute includes include at least one of an income of thehousehold, a language spoken in the household, a number of members ofthe household, or a number of children of the household.
 3. The systemof claim 1, wherein the second demographic attribute includes at leastone of an age of at least one member of the household, a gender of atleast one member of the household, a race of at least one member of thehousehold, an ethnicity of at least one member of the household, or aneducation level of at least one member of the household.
 4. The systemof claim 1, wherein the third demographic attribute includes a number oftelevision sets.
 5. The system of claim 1, wherein each viewing event inthe panelist viewing data includes an identification of a media, anadvertisement, a website, an app, a network, and/or a program associatedwith the viewing event and a time duration that the panelist householdwas exposed to the viewing event.
 6. The system of claim 1, wherein eachviewing event occurs on a television, a mobile phone, a tablet, or asmart watch.
 7. The system of claim 1, wherein the instructions, whenexecuted, further cause the at least one processor to generate a quotabased on a number of households with the demographic attributes of theRHU relative to a number of households in the market, wherein a numberof matching panelist households assigned to each RHU is based on thequota.
 8. The system of claim 7, wherein the instructions, whenexecuted, further cause the at least one processor to stop assigningpanelists households to an RHU based on the number of matching panelisthouseholds meeting the quota of the RHU.
 9. The system of claim 8,wherein the instructions, when executed, further cause the at least oneprocessor to duplicate the monitored viewing data of the first panelisthousehold for an RHU based on the number of matching panelist householdsassigned to the RHU being less than the quota after a plurality ofpanelist households including the first and second panelist householdsare assigned.
 10. The system of claim 1, wherein the instructions, whenexecuted, further cause the at least one processor to determine that thefirst, second, and third panelist households are active based on viewingdata accessed from a predetermined period of time, wherein only activepanelist households are assigned to the RHUs.
 11. The system of claim 1,wherein the population data is received from one or more of a creditbureau and a census bureau.
 12. A computer-implemented process,comprising: monitoring, via panelist software executing on a panelistdevice, viewing events displayed on the panelist device for a panelisthousehold and generating panelist viewing data based on the monitoredviewing events; accessing population data including classes for each offirst, second, and third demographic attributes of households in amarket; generating an array of representative household units (RHUs)including a first RHU, wherein the RHUs are each assigned a class foreach of the first, second, and third demographic attributes; accessing,based on the panelist viewing data, a panelist class of each of thefirst, second, and third demographic attributes for first, second, andthird panelist households; matching the panelist classes of the first,second, and third demographic attributes for the first panelisthousehold to the respective classes of the first, second, and thirddemographic attributes for the first RHU; assigning the first panelisthousehold to the first RHU; determining that the panelist classes of thefirst, second, and third demographic attributes for the second panelisthousehold do not match the respective classes of the first, second, andthird demographic attributes for any RHU of the array of RHUs; matchingthe panelist class of the first and second demographic attributes forthe second panelist household to the class of the first and seconddemographic attributes for the first RHU; assigning the second panelisthousehold to the first RHU without considering the third demographicattribute; determining that the panelist classes of the first and seconddemographic attributes for the third panelist household do not match therespective classes of the first and second demographic attributes forany RHU of the array of RHUs; matching the panelist class of the firstdemographic attribute for the third panelist household to the class ofthe first RHU for the first demographic attribute; assigning the thirdpanelist household to the first RHU without considering the second andthird demographic attributes; and determining, based at least on thepanelist viewing data and the assignment of the first panelisthousehold, the second panelist household, and the third panelisthousehold to the first RHU, a number of unique views of content withoutadditional adjustment.
 13. The computer-implemented process of claim 12,wherein the first demographic attribute includes include at least one ofan income of the household, a language spoken in the household, a numberof members of the household, or a number of children of the household.14. The computer-implemented process of claim 12, wherein the seconddemographic attribute includes at least of an age of at least one memberof the household, a gender of at least one member of the household, arace of at least one member of the household, an ethnicity of at leastone member of the household, or an education level of at least onemember of the household.
 15. The computer-implemented process of claim12, further comprising generating a quota based on a number ofhouseholds with the demographic attributes of the RHU relative to anumber of households in the market, wherein a number of matchingpanelist households assigned to each RHU is based on the quota.
 16. Thecomputer-implemented process of claim 12, wherein the population data isreceived from one or more of a credit bureau and a census bureau.
 17. Anon-transitory computer-readable medium comprising computer-executableinstructions which, when executed by at least one processor, cause theat least one processor to: monitor, via panelist software executing on apanelist device, viewing events displayed on the panelist device for apanelist household and generate panelist viewing data based on themonitored viewing events; access population data including classes foreach of first, second, and third demographic attributes of households ina market; generate an array of representative household units (RHUs)including a first RHU, wherein the RHUs are each assigned a class foreach of the first, second, and third demographic attributes; access,based on the panelist viewing data, a panelist class of each of thefirst, second, and third demographic attributes for first, second, andthird panelist households; match the panelist classes of the first,second, and third demographic attributes for the first panelisthousehold to the respective classes of the first, second, and thirddemographic attributes for the first RHU; assign the first panelisthousehold to the first RHU; determine that the panelist classes of thefirst, second, and third demographic attributes for the second panelisthousehold do not match the respective classes of the first, second, andthird demographic attributes for any RHU of the array of RHUs; match thepanelist class of the first and second demographic attributes for thesecond panelist household to the class of the first and seconddemographic attributes for the first RHU; assign the second panelisthousehold to the first RHU without considering the third demographicattribute; determine that the panelist classes of the first and seconddemographic attributes for the third panelist household do not match therespective classes of the first and second demographic attributes forany RHU of the array of RHUs; match the panelist class of the firstdemographic attribute for the third panelist household to the class ofthe first RHU for the first demographic attribute; assign the thirdpanelist household to the first RHU without considering the second andthird demographic attributes; and determine, based at least on thepanelist viewing data and the assignment of the first panelisthousehold, the second panelist household, and the third panelisthousehold to the first RHU, a number of unique views of content withoutadditional adjustment.
 18. The non-transitory computer-readable mediumof claim 17, wherein the first demographic attribute includes includeone or more of an income of the household, a language spoken in thehousehold, a number of members of the household, and a number ofchildren of the household.
 19. The non-transitory computer-readablemedium of claim 17, wherein the second demographic attribute includesone or more of an age of at least one member of the household, a genderof at least one member of the household, a race of at least one memberof the household, an ethnicity of at least one member of the household,and an education level of at least one member of the household.
 20. Thenon-transitory computer-readable medium of claim 17, wherein thepopulation data is received from one or more of a credit bureau and acensus bureau.